Email Spam Detection Using Machine Learning and Feature Optimization Method

Email communication has been stated to be the most affordable, economical, and quickest communication way nowadays. Although there are significant perks of emails, sadly, its practice has been baffled by the vast amount of unsolicited and often deceitful emails. And these extreme amounts of spam are decreasing the essence of information present on the Internet and causing concern among users. Many spam detection models have been suggested and experimented with within the literature; however, the listed accuracy showed that more could be done in this area to improve accuracy. This work describes how to detect spam emails using machine learning based on words, numbers, and characters in the emails' content. We have used some prevalent machine learning models (Naive Bayes', Neural Network, KNN, Tree, Logistic Regression) and compared them to classify emails. This paper's primary focus is obtaining optimal accuracy with the help of limited features out of 57.